t-SNE or not t-SNE, that is the question

T-distributed Stochastic Neighbor Embedding (t-SNE) has become an extremely popular algorithm for low- dimensional visualization of high dimensional data. While it is acknowledged that it is highly sensitive to its parameters, it continues to be used extensively by the machine learning community, with `intuition' an accepted basis for embedding selection. In this paper, we will illustrate and explain why t-SNE is not a distance preserving algorithm, but rather order preserving, with the cardinality of the order proportional to the perplexity parameter. We compare and contrast t-SNE with Sammon Nonlinear Mappings locally using Kruskal Stress and Spearman Rank Correlation measures.